PPAD

This is the official codebase for the paper A Poisson-process AutoDecoder for X-ray Sources. The code and README is relatively poorly documented and some features are obsolete (like LSTMEncoder and TransformerEncoder). The code only supports single GPU at the moment. Feel free to contact Yanke Song ([email protected]) if you have any questions.

pip install requirements.txt

Inference

For inference on new event files, you would first want to create a data list, which is a python list of dictionaries. Each dictionary contains the key "event_list", which stores a two-dimensional numpy array recording the arrival time and energy of photons (an event list), and other variables for your convenience, like "obsreg_id".

Then run the following code, replacing variables in ${} with you actual paths

python inference.py --checkpoint ${checkpoint} --data ${data} --save_location ${save_location}

Training

For training with the data we prepared, refer to the training code below. You would need to place the relevant data pkl files at corresponding locations.

B=16
starting_epoch=0
num_epochs=100
checkpoint_every=50
latent_size=8
hidden_size=512
hidden_blocks=5
data_type="large_filtered"
model_type="decoder"
TV_type="separate"
lam_latent=1
lam_TV=10
lr=0.00001

# First stage training, on a filtered subset
python main.py --data_type "${data_type}" --model_name "${model_name}" --model_type "${model_type}" --TV_type "${TV_type}" --random_shift --starting_epoch "${starting_epoch}" --num_epochs "${num_epochs}" --latent_size ${latent_size} --hidden_size ${hidden_size} --hidden_blocks ${hidden_blocks} --checkpoint_every ${checkpoint_every} --lam_TV ${lam_TV} --B "${B}" --lr ${lr} --lam_latent ${lam_latent} --thin_resnet 

# For second stage training, change data_type to "large", and add the following checkpoint info
--finetune_checkpoint "${finetune_checkpoint}" --latent_only

# For third stage training, add the following checkpoint info
--finetune_checkpoint2 "${finetune_checkpoint2}"

If you want to train the model yourself, please refer to main.py for the training script and relevant options. You probably want to modify the training code to fit your need.